比较三种数据驱动方法在超市库存优化中的表现。
A Study of Data-driven Methods for Inventory Optimization
- 用时间序列、随机森林和深度强化学习优化三类库存模型。
- 深度强化学习在适应市场变化上表现最优，降低库存成本。
- 适合关注供应链效率与数据决策的管理者阅读。
本文对三种算法（时间序列、随机森林（RF）和深度强化学习）在三种库存模型（缺货模型、双源采购模型和多级库存模型）中的应用进行了全面分析。研究以超市场景为背景，旨在评估数据驱动方法的有效性、潜力及当前挑战。通过多个关键绩效指标（如预测准确率、对市场变化的适应性、库存成本和客户满意度）对比各算法表现。利用数据可视化工具与统计指标进行评估，揭示了明显趋势与模式，帮助管理者实时监控算法性能并深入分析库存波动原因。该分析有助于精准定位供应链中的低效环节与改进空间。
原文摘要 · Abstract (English)
This paper shows a comprehensive analysis of three algorithms (Time Series, Random Forest (RF) and Deep Reinforcement Learning) into three inventory models (the Lost Sales, Dual-Sourcing and Multi-Echelon Inventory Model). These methodologies are applied in the supermarket context. The main purpose is to analyse efficient methods for the data-driven. Their possibility, potential and current challenges are taken into consideration in this report. By comparing the results in each model, the effectiveness of each algorithm is evaluated based on several key performance indicators, including forecast accuracy, adaptability to market changes, and overall impact on inventory costs and customer satisfaction levels. The data visualization tools and statistical metrics are the indicators for the comparisons and show some obvious trends and patterns that can guide decision-making in inventory management. These tools enable managers to not only track the performance of different algorithms in real-time but also to drill down into specific data points to understand the underlying causes of inventory fluctuations. This level of detail is crucial for pinpointing inefficiencies and areas for improvement within the supply chain.
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